On Sat, Mar 1, 2008 at 2:43 PM, devnew@gmail.com <devnew@gmail.com> wrote:
> hi
> i have a set of images of faces which i make into a 2d array using
> numpy.ndarray
> each row represents a face image
> faces=
> [[ 173. 87. ... 88. 165.]
> [ 158. 103. .. 73. 143.]
> [ 180. 87. .. 55. 143.]
> [ 155. 117. .. 93. 155.]]
>> from which i can get the mean image =>
> avgface=average(faces,axis=0)
> and calculate the adjustedfaces=faces-avgface
>> now if i apply svd() i get
> u, s, vt = linalg.svd(adjustedfaces, 0)
> # a member posted this
> facespace=vt.transpose()
>> and if i calculate covariance matrix
> covmat=matrix(adjustedfaces)* matrix(adjustedfaces).transpose()
> eval,evect=eigh(covmat)
> evect=sortbyeigenvalue(evect) # sothat largest eval is first
> facespace=evect* matrix(adjustedfaces)
>> what is the difference btw these 2 methods?
See my answer, in your other post
> apparently they yield
> different values for the facespace.
Not really.
> which should i follow?
The svd is a little less efficient and slightly slower. However it is
clear in implementation and may, in some rare situations, be more
precise.
> is it possible to calculate eigenvectors using svd()?
Again, see me other response.
>> thanks
> D
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